6178 وظيفة Ai Data Architect Us قريبًا مني

Role Overview

As an AI & Data Architect, you will define the technical architecture for large-scale AI and data platforms that power both OCI services and AI-enabled experiences across Oracle products. You will establish reference architectures, canonical APIs, integration patterns, and platform primitives that product teams can build on rather than recreate independently.

You will work across distributed cloud infrastructure, AI systems, databases, data engineering, search, enterprise applications, networking, security, and developer platforms. The role requires strong systems thinking, deep software and data architecture experience, practical knowledge of modern AI systems, and the ability to influence technical direction across multiple engineering organizations without relying on organizational authority.

Oracle Product & Platform Scope

The architecture is expected to span OCI and the broader Oracle portfolio. Representative platforms include:

Architecture Layer

Representative Oracle Platforms / Capabilities

AI & Agent Platform

OCI Generative AI (Responses API, hosted agentic applications, and Agents/RAG); OCI Data Science; OCI AI Services (Language, Speech, Vision, and Document Understanding).

Database, Data & Retrieval

Oracle AI Database 26ai / Autonomous AI Database; AI Vector Search; Select AI / NL2SQL; Oracle AI Data Platform; GoldenGate; Object Storage, Streaming, Data Integration, Data Flow, and OpenSearch.

Applications & Analytics

Fusion Cloud Applications; AI Agent Studio / Fusion Agentic Applications; Oracle Analytics Cloud / Fusion Data Intelligence; Oracle Integration; Oracle APEX.

Cross-Oracle Consumers

Reusable capabilities for Oracle SaaS and industry products, NetSuite, Oracle Health, partner solutions, and customer applications.

This list is representative, not exhaustive; the role is expected to create reusable architecture across product boundaries, not own every product implementation

What You'll Do

• Define long-term architecture and technical strategy for Oracle-wide AI and data platform capabilities, with OCI as the core cloud foundation and clean integration into Oracle Database, Applications, Analytics, and Integration products.

• Design scalable platform architectures for generative AI, agentic AI, classical machine learning, structured and unstructured data, enterprise search, knowledge systems, and conversational data access.

• Architect systems for:

– Foundation-model access, model routing, inference, embeddings, reranking, fine-tuning, prompt/context management, and model lifecycle.

– Agent runtimes, multi-agent orchestration, planning, memory, tool execution, workflow integration, human approvals, and secure delegation.

– Retrieval-augmented generation (RAG), knowledge bases, semantic and hybrid search, query rewriting, reranking, grounding, provenance, and citations.

– Enterprise knowledge management including ingestion, parsing, chunking, metadata, taxonomy, ontology, business glossary, lifecycle, and entitlement-aware retrieval.

– NL2SQL and conversational analytics, including schema/semantic grounding, metadata enrichment, SQL generation and validation, permission-aware execution, and natural-language narration of results.

– AI-ready data platforms supporting batch, streaming, change data capture, lakehouse patterns, data products, feature/embedding generation, and low-latency serving.

– AI evaluation and observability covering quality, hallucination/grounding, retrieval relevance, tool-call success, agent completion, latency, reliability, safety, and cost.

– AI security and governance including identity, authorization, tenant isolation, private networking, secrets, auditability, data residency, prompt-injection defenses, exfiltration controls, and policy enforcement.

– Multimodal AI experiences spanning text, documents, images, speech, and structured enterprise data.

– Developer platforms, APIs, SDKs, reference implementations, and reusable components that let Oracle teams and customers build AI applications consistently.

• Define architecture patterns that combine OCI Generative AI and agent capabilities with Oracle AI Database 26ai, Autonomous AI Database, AI Vector Search, Select AI, Oracle AI Data Platform, GoldenGate, OpenSearch, Object Storage, and other OCI data services.

• Establish reusable integration patterns between OCI AI services and Oracle Fusion Cloud Applications, AI Agent Studio / Fusion Agentic Applications, Oracle Analytics Cloud, Fusion Data Intelligence, Oracle Integration, and adjacent Oracle products.

• Drive semantic architecture across structured data and enterprise knowledge so agents and AI assistants understand business concepts, relationships, permissions, and source-of-truth boundaries rather than only raw schemas or documents.

• Define retrieval architecture choices across Oracle AI Database vector search, OCI Search with OpenSearch, managed knowledge bases, file search, and federated enterprise sources, including guidance for hybrid retrieval, ranking, freshness, and ACL enforcement.

• Partner with database, data, AI science, applications, analytics, security, and infrastructure teams to operationalize new model capabilities without fragmenting the platform architecture.

• Define canonical APIs, schemas, contracts, tool interfaces, event patterns, and interoperability standards for agents, models, knowledge sources, data products, and enterprise applications.

• Establish patterns for hybrid and distributed deployments where data or inference must remain close to regulated, sovereign, customer, or on-premises environments.

• Evaluate emerging AI, data, search, agent, and model-serving technologies and determine where Oracle should build, integrate, standardize, or partner.

• Drive technical direction across multiple engineering organizations and mentor senior engineers and architects.

Basic Qualifications

• BS, MS, or PhD in Computer Science, Data/AI, Electrical Engineering, Mathematics, or a related technical field.

• 15+ years of software engineering experience, with significant experience defining architecture for distributed, data-intensive, or cloud platforms operating at large scale.

• Deep expertise in cloud-native architecture, APIs, event-driven systems, microservices, asynchronous workflows, reliability, and multi-tenant platform design.

• Strong understanding of modern AI application architecture, including LLMs, embeddings, RAG, agentic systems, tool use, evaluation, and production operationalization.

• Strong data architecture background spanning relational and non-relational databases, data lakes/lakehouses, streaming, CDC, search, metadata, governance, and structured/unstructured data.

• Experience designing secure enterprise platforms with strong authentication, authorization, distributed identity, policy enforcement, encryption, auditability, and data isolation.

• Strong programming background in Java, Python, Go, C++, Rust, or similar languages, with the ability to reason about implementation tradeoffs and platform APIs.

• Experience with Kubernetes, containers, Linux, networking, service-to-service security, observability, and modern cloud infrastructure.

• Excellent written and verbal communication skills, including the ability to produce architecture documents, reference designs, and clear technical decisions for senior engineering and product audiences.

Preferred Qualifications

Experience in several of the following areas:

• OCI architecture and services, particularly OCI Generative AI, agentic capabilities, OCI Data Science, OCI AI Services, OKE, IAM, networking, observability, and security services.

• Oracle AI Database 26ai, Autonomous AI Database, AI Vector Search, Select AI / NL2SQL, in-database AI, SQL/JSON, graph, spatial, or related database capabilities.

• Oracle AI Data Platform, OCI GoldenGate, streaming/CDC, data integration, lakehouse architectures, metadata/catalog systems, and enterprise data governance.

• Oracle Fusion Cloud Applications, AI Agent Studio / Fusion Agentic Applications, Oracle Analytics Cloud, Fusion Data Intelligence, Oracle Integration, or APEX.

• Production RAG and enterprise search systems using vector search, keyword search, hybrid retrieval, reranking, semantic caching, retrieval evaluation, and relevance tuning.

• Enterprise knowledge platforms including content ingestion, document processing, taxonomies, ontologies, knowledge graphs, metadata enrichment, lifecycle, and access-aware retrieval.

• NL2SQL / text-to-SQL systems, semantic models, business metrics layers, schema linking, query planning, SQL safety, and conversational analytics.

• Agentic systems with tool/function calling, agent memory, workflow engines, multi-agent coordination, human-in-the-loop controls, and open agent/tool protocols such as MCP.

• Model serving and inference architecture, GPU infrastructure, model optimization, fine-tuning, embeddings, rerankers, multimodal models, and private/bring-your-own-model deployments.

• AI evaluation, red teaming, safety, responsible AI, model/data lineage, policy-as-code, audit trails, and enterprise compliance controls.

• Building reusable platform capabilities consumed by multiple product teams, business units, or external developers.

Ideal Candidate

The ideal candidate combines deep software and data architecture experience with practical understanding of how modern AI systems behave in production. You are equally comfortable discussing model and retrieval quality, database semantics, distributed-systems failure modes, data freshness, identity and authorization, developer APIs, and the operating model required to run AI safely at enterprise scale.

You do not treat AI as a standalone feature. You think in terms of platform primitives, authoritative data, semantic context, governed knowledge, reusable agent capabilities, and product integration. You can simplify a fragmented landscape into a small number of coherent architecture patterns and influence teams through technical leadership rather than organizational authority.

You have a track record of building platforms that other engineers and product teams build products on.

Areas of Technical Ownership

• Enterprise AI platform architecture

• Generative AI and foundation-model integration

• Agentic AI runtime, orchestration, memory, and tools

• RAG, enterprise knowledge, search, and retrieval

• NL2SQL and conversational data access

• AI-ready data platforms and real-time data pipelines

• Semantic layers, metadata, ontology, and knowledge architecture

• Oracle AI Database / vector and in-database AI patterns

• AI security, governance, evaluation, and observability

• Multimodal AI and document intelligence

• Platform APIs, SDKs, developer experience, and reference architectures

• Cross-Oracle product integration and interoperability

• System scalability, reliability, cost efficiency, and operational readiness

What Success Looks Like

• Defined Oracle's reference architecture for enterprise AI and data platforms spanning OCI and major Oracle product families.

• Established canonical architecture for agentic AI on OCI, including model access, orchestration, tools, memory, identity, approvals, observability, evaluation, and governance.

• Created a reusable enterprise knowledge and retrieval architecture with high-quality hybrid search, access-aware RAG, provenance, citations, freshness, and lifecycle management.

• Established an Oracle-native NL2SQL architecture using governed metadata and semantic context, with Oracle AI Database / Select AI as a core building block and clear integration into analytics and application experiences.

• Defined how Oracle AI Data Platform, Oracle AI Database, GoldenGate, streaming, object storage, and data integration services combine to create trustworthy AI-ready data foundations.

• Created cross-product integration patterns connecting OCI AI capabilities with Fusion Applications, AI Agent Studio / Fusion Agentic Applications, Oracle Analytics, Integration, and other Oracle product teams.

• Standardized APIs, SDKs, reference implementations, and architecture guardrails so multiple teams can ship AI capabilities faster without duplicating foundational infrastructure.

• Established measurable production standards for AI quality, retrieval relevance, safety, latency, reliability, cost, and auditability.

• Influenced Oracle's long-term AI, data, knowledge, and agent platform strategy and product direction across organizational boundaries.